Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier

Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.